{"type":"ScholarlyArticle","title":"FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning","authors":[{"name":"Liping Yi","slug":"liping-yi","given":"Liping","family":"Yi","affiliation":"College of Computer Science , TMCC, SysNet, DISSec , GTIISC , Nankai University , Tianjin , China; College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Han Yu","slug":"han-yu","given":"Han","family":"Yu","affiliation":"College of Computing and Data Science , Nanyang Technological University , Singapore"},{"name":"Zhuan Shi","slug":"zhuan-shi","given":"Zhuan","family":"Shi","affiliation":"Artificial Intelligence Laboratory , École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland"},{"name":"Gang Wang","slug":"gang-wang","given":"Gang","family":"Wang","affiliation":"College of Computer Science , TMCC, SysNet, DISSec , GTIISC , Nankai University , Tianjin , China"},{"name":"Xiaoguang Liu","slug":"xiaoguang-liu","given":"Xiaoguang","family":"Liu","affiliation":"College of Computer Science , TMCC, SysNet, DISSec , GTIISC , Nankai University , Tianjin , China"},{"name":"Lizhen Cui","slug":"lizhen-cui","given":"Lizhen","family":"Cui","affiliation":"School of Software , Shandong University , Jinan , China"},{"name":"Xiaoxiao Li","slug":"xiaoxiao-li","given":"Xiaoxiao","family":"Li","affiliation":"Department of Electrical and Computer Engineering , The University of British Columbia , Vancouver , BC , Canada"}],"abstract":"Federated learning (FL) is a privacy-preserving collaboratively machine learning paradigm. Traditional FL requires all data owners (a.k.a. FL clients) to train the same local model. This design is not well-suited for scenarios involving data and/or system heterogeneity. Model-Heterogeneous Personalized FL (MHPFL) has emerged to address this challenge. Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs. To address these limitations, we propose the Federated Semantic Similarity Aggregation (FedSSA) approach for supervised classification tasks, which splits each client's model into a heterogeneous (structure-different) feature extractor and a homogeneous (structure-same) classification header. It performs local-to-global knowledge transfer via semantic similarity-based header parameter aggregation. In addition, global-to-local knowledge transfer is achieved via an adaptive parameter stabilization strategy which fuses the seen-class parameters of historical local headers with that of the latest global header for each client. FedSSA does not rely on public datasets, while only requiring partial header parameter transmission to save costs. Theoretical analysis proves the convergence of FedSSA. Extensive experiments present that FedSSA achieves up to 3.62% higher accuracy, 15.54 times higher communication efficiency, and 15.52 times higher computational efficiency compared to 7 state-of-the-art MHPFL baselines.","doi":"10.24963/ijcai.2024/594","url":"https://ijcai.org/proceedings/2024/594","pdf":"https://ijcai.org/proceedings/2024/0594.pdf","markdown":"https://ijcai.org/proceedings/2024/594.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/594","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":594,"pages":{"first":5371,"last":5379},"keywords":["Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Ahn et al., 2019] Jin-Hyun Ahn, Osvaldo Simeone, and Joonhyuk Kang. 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